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About This Role
ABOUT THE ORGANIZATION
Sun Chemical, a member of the DIC Group, is a leading producer of packaging and graphic solutions, color and display technologies, functional products, electronic materials, and products for the automotive and healthcare industries. Together with DIC, Sun Chemical is continuously working to promote and develop sustainable solutions to exceed customer expectations and better the world around us. With combined annual sales of more than $8.5 billion and 22,000+ employees worldwide, the DIC Group companies support a diverse collection of global customers.
Sun Chemical Corporation is a subsidiary of Sun Chemical Group Coöperatief U.A., the Netherlands, and is headquartered in Parsippany, New Jersey, U.S.A. For more information, please visit our website at www.sunchemical.com or connect with us on LinkedIn or Twitter
The Role
The Director, AI Transformation & Intelligent Business Enablement will lead Sun Chemical / DIC Group’s enterprise approach to applied artificial intelligence, translating AI capability into measurable business value across commercial, manufacturing, and functional teams. This role bridges strategy and execution: identifying high-value AI opportunities, building the operating model and governance to scale them responsibly, and partnering across a complex global, matrixed organization to drive adoption. The successful candidate will reframe AI from a technology initiative to a decision-intelligence capability that strengthens the way the enterprise plans, decides, and operates.
Key Responsibilities:
- Define and own the enterprise AI transformation roadmap, prioritizing use cases by business value, feasibility, and risk.
- Build and lead a federated AI Center of Excellence (CoE) model — balancing central governance, data, and platform standards with embedded business-unit ownership of use cases.
- Partner with business and functional leaders to identify and quantify opportunities for revenue growth, margin improvement, productivity enhancement and risk reduction through AI-enabled solutions.
- Partner with HR and business and functional leadership to assess workforce impacts, redesign processes and develop future-state skills and capability requirements.
- Develop the AI governance framework, including model risk, data privacy, ethical use guidelines, and compliance alignment with global regulatory requirements (e.g., EU AI Act).
- Partner with legal, privacy, IT, security and risk management leaders to establish controls addressing AI-related risks.
- Lead cross-functional AI pilots and scale successful initiatives enterprise-wide, working closely with IT, Data & Analytics, and business-unit leadership.
- Design and deliver AI literacy and capability-building programs to support adoption across varying levels of technical fluency, including for non-technical business leaders.
- Build the business case, ROI tracking, and value-realization framework and tracking KPIs for AI investments; report progress to senior leadership and SLT.
- Navigate and align AI strategy across the Sun Chemical and DIC matrix, accounting for cultural, regulatory, and operational differences.
- Stay ahead of emerging AI capabilities and vendor landscape, evaluating build-vs-buy-vs-partner decisions for enterprise tools and platforms.
- Act as a trusted advisor and change agent, helping leaders and teams move through the organizational and cultural shifts AI adoption requires.
Required Qualifications
- 10+ years of progressive experience in business / strategy transformation, leveraging technology and data capabilities, or in technology leadership roles; 3+ years owning direct AI/ML or advanced analytics / technical platform programs.
- Demonstrated success building and scaling an AI or data analytics Center of Excellence, and/or leading enterprise-wide digital/AI transformation initiatives.
- Strong business acumen with the ability to translate technical AI capability into commercial and operational value propositions.
- Experience working within a global, matrixed organization; comfort navigating cross-cultural and cross-regional stakeholder dynamics.
- Working knowledge of AI governance, data privacy, and ethical AI frameworks.
- Experience leading transformation initiatives.
- Exceptional stakeholder management and executive communication skills, including experience presenting to senior leadership and boards.
- Bachelor’s degree required; MBA or advanced degree in a related field (computer or data science, business, engineering) preferred.
Preferred Experience
- Background in chemicals, manufacturing, or industrial B2B sectors.
- Familiarity with change management methodologies and experience leading large-scale technical capability-building programs.
- Expertise with major enterprise AI platforms (e.g., Microsoft Copilot, Azure AI, AWS Bedrock, Anthropic Claude, Gemini or similar) and modern data architecture concepts.
- Prior experience partnering with HR/Talent Management functions on workforce capability and AI-adjacent skills strategy.
What Success Looks Like in the First 12 Months
- A clear, prioritized AI opportunity roadmap endorsed by senior leadership.
- At least 2–3 high-impact AI use cases moved from pilot to scaled deployment with measurable business outcomes.
- A functioning governance framework and CoE operating model in place.
- Demonstrated improvement in cross-functional AI literacy and adoption, measured through engagement and capability assessments.
EQUAL EMPLOYMENT OPPORTUNITY
It is Sun’s policy that applicants for employment are recruited, selected, and hired on the basis of individual merit and ability with respect to the position being filled. Sun prohibits discrimination on the basis of race, color, religion, creed, national origin, age, gender, sexual orientation, gender identity, marital or veteran status, disability, or any other status protected by applicable law.
Employment agencies are an important component of our talent acquisition strategy, and we value the partnerships we have built with our preferred vendors. We will not accept unsolicited resumes from employment agencies for any employment opportunity. All resumes submitted by search firms to any employee at Sun Chemical or directly to hiring managers in any form without a signed contract on file, and search engagement for that position, will be deemed unsolicited in nature, and no fee will be paid in the event the candidate is hired as a result of the referral or through other means. Additionally, any resume submitted without a contract in place will be considered property of Sun Chemical and no fees will be paid to the agency if the candidate is hired.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Sun Chemical, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Sun Chemical AI Hiring
Sun Chemical has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Concord, CA, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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